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334 results for “Cas9”
Verification of library complexity in the HEK-Cas9 sublibraries - sequence data of the generated sublibraries A and B
<p>Sequence data of the generated HEK-Cas9 sublibraries A and B, linked to the manuscript 10.1128/mbio.01925-24: The <em>Bordetella</em> effector protein BteA induces host cell death by disruption of calcium homeostasis by Martin Zmuda, Eliska Sedlackova, Barbora Pravdova, Monika Cizkova, Marketa Dalecka, Ondrej Cerny, Tania Romero Allsop, Tomas Grousl, Ivana Malcova, and Jana Kamanova</p>
Kymograph of Cas9 binding to λ-DNA under tension
<p>Kymograph measurement of force-stretched λ-DNA in the presence of dCas9 with Cy3-labelled crRNA between 5 and 50 pN.</p>
Data: Linking CRISPR-Cas9 interference in cassava to the evolution of editing-resistant geminiviruses
<p>Dataset of raw SMRT sequence data and processed data including alignments of virus sequences edited using CRISPR-Cas9 technology in cassava plants.</p> <p> </p>
A scalable CRISPR-Cas9 gene editing system facilitates CRISPR screens in the malaria parasite Plasmodium berghei - sequencing data
<p>This holds raw sequencing data, and extracted sgRNA counts </p>
Supplementary Movies: "Probing the dynamics of Streptococcus pyogenes Cas9 endonuclease bound to sgRNA complex using hydrogen-deuterium exchange mass spectrometry"
<p>Movies related to the article "Probing the dynamics of Streptococcus pyogenes Cas9 endonuclease bound to sgRNA complex using hydrogen-deuterium exchange mass spectrometry" in the International Journal of Molecular Science. "MD_Movie_Cas9_sgRNA_DNA" is the video of SpCas9-sgRNA-DNA complex behavior during 50 ns molecular dynamics simulation. In this movie, SpCas9 protein domains are shown in the following colors: REC lobe (green), CTD (blue), RuvC (pink), L-I-II (yellow), Arg (violet), and HNH (orange). RNA presented in cyan, and DNA -in dark blue.</p> <p>Movies "SpCas9_HeatMap" and "SpCas9-sgRNA_HeatMap" show the hydrogen exchange levels superimposed onto the protein structure obtained from MD trajectories. Relative uptake level presented at the time points of 10 s, 30 s, 1 min, 2 min, 5 min, 10 min, 30 min, 60 min, 120 min, 240 min, 360 min, and 480 min. The exchange scale is shown in a rainbow color scheme, where blue corresponds to the minimum uptake, while red corresponds to the highest observed uptake.</p>
PAM-altering SNP-based allele-specific CRISPR-Cas9 therapeutic strategies for Huntington's disease
<p>Huntington's disease (HD) is caused by an expanded CAG repeat in huntingtin (<em>HTT</em>). Since HD is dominant, and loss of <em>HTT </em>leads to neurological abnormalities, safe therapeutic strategies require selective inactivation of mutant <em>HTT</em>. Previously, we proposed a concept of CRISPR-Cas9 using mutant-specific PAM sites generated by SNPs to selectively inactivate mutant <em>HTT</em>. Aiming at revealing suitable targets for clinical development, we analyzed the largest HD genotype dataset to reveal target <strong>P</strong>AM-<strong>a</strong>ltering <strong>S</strong>NPs (PAS) and subsequently evaluated their allele specificities. The gRNAs based on the PAM sites generated by rs2857935, rs16843804, and rs16843836 showed high levels of allele specificity in patient-derived cells. Simultaneous use of two gRNAs based on rs2857935-rs16843804 or rs2857935-rs16843836 produced selective genomic deletions in mutant <em>HTT </em>and prevented the transcription of mutant <em>HTT </em>mRNA without impacting the expression of normal counterpart or re-integration of the excised fragment elsewhere in the genome. RNAseq and off-target analysis confirmed high levels of allele specificity and the lack of recurrent off-targeting. Approximately 60% of HD subjects are eligible for mutant-specific CRISPR-Cas9 strategies of targeting one of these 3 PAS in conjunction with one non-allele-specific site, supporting high applicability of PAS-based allele-specific CRISPR approaches in the HD patient population.</p>
CRISPR-Cas9 screen upon Olaparib+CldU treatment
<p>DNA single-strand breaks (SSBs) disrupt DNA replication and induce chromosome breakage. However, whether SSBs induce chromosome breakage when present in nascent strands behind replication forks or template strands ahead of replication forks is unclear. To address this question, we exploited an exquisite sensitivity of SSB repair-defective human cells lacking PARP activity or XRCC1 to the thymidine analog 5-chloro-2'-deoxyuridine (CldU). We show that incubation with CldU in these cells results in chromosome breakage, sister chromatid exchange, and cytotoxicity by a mechanism that depends on the S phase activity of uracil DNA glycosylase (UNG). Importantly, we show that CldU incorporation in one cell cycle is cytotoxic only during the following cell cycle when it is present in template DNA. In agreement with this, while UNG induces SSBs both in nascent strands behind replication forks and in template strands ahead of replication forks, only the latter triggers fork collapse and chromosome breakage. Finally, we show that BRCA-defective cells are hypersensitive to CldU, either alone and/or in combination with PARP inhibitor, suggesting that CldU may have clinical utility.</p>
CRISPR-Cas9 off-targeting assessment with nucleic acid duplex energy parameters
<p>CRISPR-Cas9 off-targeting assessment with nucleic acid duplex energy parameters</p> <p>Collected and generated data for the paper</p> <p>## Data Tables</p> <p>Off-target score data for the ROC analysis using Haeussler dataset [2].</p> <p>Data from the table below is used to generate the Figure-2, Table-1 and Supplementary Figure-1 in the corresponding paper [1]. Don't forget to cite the corresponding studies as well if you use this table.</p> <ul> <li><strong>Haeussler_mm6_scores.csv.gz</strong>: This table includes the off-targeting scores of 1167036 off-target sequences, computed with CRISPRoff[1], CCTop[3], CFD[4], Cropit[5], Elevation (Elevation-score)[6], MIT[2,7] and VfoldCAS[8] methods. Off-target data has been taken from the Haeussler dataset [2].</li> </ul> <p>Analysis with CIRCLE-seq dataset [9]</p> <p>Data in all the three tables below has been generated to analyze the CIRCLE-seq dataset [9]. This data is further used to generate the Figure-3, Figure-4, and Supplementary Figure-4 in the corresponding paper. Don't forget to cite the corresponding studies as well if you use these tables.</p> <ul> <li> <p><strong>CIRCLEseq_known_off_scores.csv.gz</strong>: This table is used when generating the Figure-3 in the paper. It includes the 7 different off-targeting scores of CIRCLE-seq reported off-target sequences and the read counts from CIRCLE-seq experiments.</p> </li> <li> <p><strong>CIRCLEseq_mm6_off_scores.csv.gz</strong>: This table is used when generating the Figure-4 in the paper. It includes the 7 different off-targeting scores of RIsearch2(v2.1)[10] based off-target predictions for CIRCLE-seq gRNAs.</p> </li> <li> <p><strong>CIRCLEseq_specificities.csv.gz</strong>: This table is used when generating the Supplementary Figure-4 in the supplementary document of the paper. It includes the specificty scores of CIRCLE-seq gRNAs, computed with CRISPRspec[1], MIT[2,7], MIT*[1,2,7] and Elevation (Elevation-aggregate)[6] methods.</p> </li> </ul> <p>Analysis with SITE-seq dataset [11]</p> <p>Data in all the three tables below has been generated to analyze the SITE-seq dataset [11]. This data is further used to generate the Figure-5, Supplementary Figure-2 and Supplementary Figure-3 in the corresponding paper. Don't forget to cite the corresponding studies as well if you use these tables.</p> <ul> <li> <p><strong>SITEseq_known_off_scores.csv.gz</strong>: This table is used when generating the Supplementary Figure-2 in the supplementary document of the paper. It includes the 7 different off-targeting scores of SITE-seq reported off-target sequences and the read counts from SITE-seq experiments.</p> </li> <li> <p><strong>SITEseq_mm6_off_scores.csv.gz</strong>: This table is used when generating the Supplementary Figure-3 in the supplementary document of the paper. It includes the 7 different off-targeting scores of RIsearch2(v2.1) based off-target predictions for SITE-seq gRNAs.</p> </li> <li> <p><strong>SITEseq_specificities.csv.gz</strong>: This table is used when generating the Figure-5 in the paper. It includes the 4 different specificty scores of SITE-seq gRNAs.</p> </li> </ul> <p>Specificity-Efficiency Analysis</p> <p>This data is used to generate the Figure-6 and Supplementary Figure-5 in the corresponding paper. Don't forget to cite the corresponding studies as well if you use these tables.</p> <ul> <li><strong>Doench_Wang_specificity_grps.csv.gz</strong>: This table includes the specificity group of 3802 gRNA/on-target sequences, computed with CRISPRspec and MIT methods. gRNA sequence and modulation frequency data have been taken from the Haeussler dataset [2].</li> </ul> <p>## Citation</p> <p>If you find this data useful for your research, please cite the following works where appropriate:</p> <ol> <li>[Our citation comes here]</li> <li>Haeussler, M., Schonig, K., Eckert, H., Eschstruth, A., Mianne, J., Renaud, J.B., Schneider-Maunoury, S., Shkumatava, A., Teboul, L., Kent, J., Joly, J.S., Concordet, J.P.: Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 17(1), 148 (2016). <a href="https://www.ncbi.nlm.nih.gov/pubmed/27380939">PMID 27380939</a></li> <li>Stemmer, M., Thumberger, T., Del Sol Keyer, M., Wittbrodt, J., Mateo, J.L.: CCTop: An Intuitive, Flexible and Reliable CRISPR/Cas9 Target Prediction Tool. PLoS ONE 10(4), 0124633 (2015). <a href="https://www.ncbi.nlm.nih.gov/pubmed/25909470">PMID 25909470</a></li> <li>Doench, J.G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E.W., Donovan, K.F., Smith, I., Tothova, Z., Wilen, C., Orchard, R., Virgin, H.W., Listgarten, J., Root, D.E.: Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat. Biotechnol. 34(2), 184–191 (2016). <a href="https://www.ncbi.nlm.nih.gov/pubmed/26780180">PMID 26780180</a></li> <li>Singh, R., Kuscu, C., Quinlan, A., Qi, Y., Adli, M.: Cas9-chromatin binding information enables more accurate CRISPR off-target prediction. Nucleic Acids Res. 43(18), 118 (2015). <a href="https://www.ncbi.nlm.nih.gov/pubmed/26032770">PMID 26032770</a></li> <li>Listgarten, J., Weinstein, M., Kleinstiver, B.P., Sousa, A.A., Joung, J.K., Crawford, J., Gao, K., Hoang, L., Elibol, M., Doench, J.G., Fusi, N.: Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs. Nature Biomedical Engineering 2, 38–47 (2018). <a href="https://www.ncbi.nlm.nih.gov/pubmed/29998038">PMID 29998038</a></li> <li>Hsu, P.D., Scott, D.A., Weinstein, J.A., Ran, F.A., Konermann, S., Agarwala, V., Li, Y., Fine, E.J., Wu, X., Shalem, O., Cradick, T.J., Marraffini, L.A., Bao, G., Zhang, F.: DNA targeting specificity of RNA-guided Cas9 nucleases. Nat. Biotechnol. 31(9), 827–832 (2013). <a href="https://www.ncbi.nlm.nih.gov/pubmed/23873081">PMID 23873081</a></li> <li>Xu, X., Duan, D., Chen, S.J.: CRISPR-Cas9 cleavage efficiency correlates strongly with target-sgRNA folding stability: from physical mechanism to off-target assessment. Sci Rep 7(1), 143 (2017). <a href="https://www.ncbi.nlm.nih.gov/pubmed/28273945">PMID 28273945</a></li> <li>Tsai, S.Q., Nguyen, N.T., Malagon-Lopez, J., Topkar, V.V., Aryee, M.J., Joung, J.K.: CIRCLE-seq: a highly sensitive in vitro screen for genome-wide CRISPR-Cas9 nuclease off-targets. Nat. Methods 14(6), 607–614 (2017). <a href="https://www.ncbi.nlm.nih.gov/pubmed/28459458">PMID 28459458</a></li> <li>Alkan, F., Wenzel, A., Palasca, O., Kerpedjiev, P., Rudebeck, A.F., Stadler, P.F., Hofacker, I.L., Gorodkin, J.: RIsearch2: suffix array-based large-scale prediction of RNA-RNA interactions and siRNA off-targets. Nucleic Acids Res. (2017). <a href="https://www.ncbi.nlm.nih.gov/pubmed/28108657">PMID 28108657</a></li> <li>Cameron, P., Fuller, C.K., Donohoue, P.D., Jones, B.N., Thompson, M.S., Carter, M.M., Gradia, S., Vidal, B., Garner, E., Slorach, E.M., Lau, E., Banh, L.M., Lied, A.M., Edwards, L.S., Settle, A.H., Capurso, D., Llaca, V., Deschamps, S., Cigan, M., Young, J.K., May, A.P.: Mapping the genomic landscape of CRISPR-Cas9 cleavage. Nat. Methods 14(6), 600–606 (2017). <a href="https://www.ncbi.nlm.nih.gov/pubmed/28459459">PMID 28459459</a></li> </ol> <p>## Contact</p> <p>ferro@rth.dk gorodkin@rth.dk</p>
Data and analysis scripts for "Mad7: An IP friendly alternative to Cas9"
<p>This is the raw data and analysis scripts for "Mad7: An IP friendly alternative to Cas9"</p>
Cas9-induced large deletions and small indels are controlled in a convergent fashion
<p>Demultiplexed reads assembled from 150PE Illumina using Pear with following settings: pear-0.9.10-bin-64 -f read1.fq -r read2.fq -n 20 -p 0.01. Unassembled PE reads (estimated at 0.1% of all reads) and reads that failed demux not included. 960 files total, corresponding to 96 wells of the library (95 control or knock-out mES clones and one well intentionally left empty) times two biological replicates times five test gRNAs (non-targetting control g33, chromosome X targetting g15, g48 and gU48 and autosome targetting g148). mES cells are derived from CAST x BL6 cross. Non-targeting control g33 fastq files are not locus-demultiplexed ie would map to g15, g48 and g148 loci. </p>
PAM-Flexible Genome Editing with an Engineered Chimeric Cas9
<p>CRISPR enzymes require a defined protospacer adjacent motif (PAM) flanking a guide RNA-programmed target site, limiting their sequence accessibility for robust genome editing applications. In this study, we recombine the PAM-interacting domain of SpRY, a broad-targeting Cas9 possessing an NRN > NYN PAM preference, with the N-terminus of Sc++, a Cas9 with simultaneously broad, efficient, and accurate NNG editing capabilities, to generate a chimeric enzyme with highly flexible PAM preference: SpRYc. We demonstrate that SpRYc leverages properties of both enzymes to specifically edit diverse NNN PAMs and disease-related loci for potential therapeutic applications. In total, the unique approaches to generate SpRYc, coupled with its robust flexibility, highlight the power of integrative protein design for Cas9 engineering and motivate downstream editing applications that require precise genomic positioning.</p>
Developing small Cas9 hybrids using molecular modeling
<p>The RNA-guided CRISPR-Cas9 from <i>Streptococcus pyogenes</i> is the best characterized enzyme for gene editing. Its large size, however, precludes it from being packaged together with its single guide (sg)RNA into a single adeno-associated virus, limiting <i>in vivo</i> applications. Here, we developed smaller Cas9 hybrids, made of the PAM interacting domain (PID) of <i>S pyogenes</i> and the catalytic domains of the smaller Cas9 orthologues, as well as sgRNA cognate hybrids. Molecular modeling revealed that the presence of a sgRNA stabilizes Cas9. Making the D10A mutation to turn Cas9 into a nickase dramatically alters its binding energy to the sgRNA, showing that the approach can identify functionally relevant changes. However, we found that the four Cas9/sgRNA hybrid pairs tested in human cells failed to edit target sequences. We conclude that <i>in silico</i> approaches can identify functional changes caused by point mutations but they may not be well suited for designing Cas9/sgRNA hybrids. </p>
PAM-altering SNP-based allele-specific CRISPR-Cas9 therapeutic strategies for Huntington's disease
Open the record for dataset details and reuse information.
CRISPR-Cas9 screen upon Olaparib+CldU treatment
Open the record for dataset details and reuse information.
Genetic editing of primary human dorsal root ganglion neurons using CRISPR-Cas9
Open the record for dataset details and reuse information.
Data from: Lung and liver editing using lipid nanoparticle delivery of a stable CRISPR-Cas9 RNP
Open the record for dataset details and reuse information.
Parallel CRISPR-Cas9 screens clarify impacts of p53 on screen performance
<p>CRISPR-Cas9 genome engineering has revolutionised high-throughput functional genomic screens. However, recent work has raised concerns regarding the performance of CRISPR-Cas9 screens using <i>TP53</i> wild-type human cells due to a p53-mediated DNA damage response (DDR) limiting the efficiency of generating viable edited cells. To directly assess the impact of cellular p53 status on CRISPR-Cas9 screen performance, we carried out parallel CRISPR-Cas9 screens in wild-type and <i>TP53</i> knockout human retinal pigment epithelial cells using a focused dual guide RNA library targeting 852 DDR-associated genes. Our work demonstrates that although functional p53 status negatively affects identification of significantly depleted genes, optimal screen design can nevertheless enable robust screen performance. Through analysis of our own and published screen data, we highlight key factors for successful screens in both wild-type and p53-deficient cells.</p>
Data from: CRISPR-Cas9 mediated CD133 knockout inhibits colon cancer invasion through reduced epithelial-mesenchymal transition
We previously reported that CD133, as a putative cancer stem cell marker, plays an important role in cell proliferation and invasion in colon cancer. To understand the role of CD133 expression in colon cancer, we evaluated the inhibitory effect of CD133 in colon cancer cells. In this study, we generated CD133knockout colon cancer cells (LoVo) using the CRISPR-Cas9 gene editing system. CD133+ colon cancer cells (LoVo) were infected with the lentiviral vector carrying CD133 gRNA and purified cell by culturing single cell colonies. CD133knockout cells was validated by western blot and flow cytometry analysis. In functional study, we observed a significant reduction in cell proliferation and colony formation in CRISPR-Cas9 mediated CD133 knockout cells in compare with control (P < 0.001). We also found the anticancer effect of stattic was dependent on CD133 expression in colon cancer cells. Although CD133knockout cells could not completely block the tumorigenic property, they showed remarkable inhibitory effects on the ability of cell migration and invasion (P < 0.001). In addition, we examined the epithelial mesenchymal transition (EMT)-related protein expression by western blot. The result clearly showed a loss of vimentin expression in CD133knockout cells. Therefore, CRISPR-Cas9 mediated CD133knockout can be an effective treatment modality for CD133+ colon cancer through reducing the characteristics of cancer stem cells.
CRISPR-Cas9 dataset of isogenic cell lines
<p>A collection of CRISPR-Cas9 isogenic (RB1, BRCA1, BRCA2) cell lines screens.</p>
NGS Data from: Improved gRNA secondary structures allow editing of target sites resistant to CRISPR-Cas9 cleavage
<p><span><span>We engineered gRNAs with highly stable hairpins in their constant parts and further enhanced their stability by chemical modifications. The 'Genome-editing Optimized Locked Design' (GOLD)-gRNA increases genome editing efficiency up to around 1000-fold (from 0.08% to 80.5%) with a mean increase across different other targets of 7.4-fold. The related NGS data is deposited </span></span>here.</p>
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